Different types of social media sites exist, wherein some of them are LinkedIn, Twitter, Facebook, Instagram, WhatsApp, etc. As the number of social media users increases, the opportunity for the user to express their feelings also increases. Twitter is a choice of many users as it not only allows the users to express their thoughts but to interact with official accounts (PMO, Defense Ministry) which can be seen with a verified tick on the website. In this thesis titled ‘Sentiment Analysis of COVID data extracted via Twitter’, multiple machine learning and deep learning techniques have been researched and implemented to perform sentiment analysis. Moreover, a novel approach using deep learning architecture has been proposed. It is based on a combination of Bidirectional Long Short Term (BiLSTM) neural networks and Convolution Neural Networks (CNN). Prior to implementing the algorithms, the data is acquired by using web-scraping techniques and/or public APIs pertaining to Twitter. A comparative analysis of the efficiency and performance of the proposed technique along with other existing approaches discovered during the literature review phase is also presented. KEYWORDS: Sentiment analysis, machine learning, deep learning, Natural Language Processing